At a Glance
- Tasks: Design and train AI models for physics simulation, optimising performance and scalability.
- Company: BeyondMath, a pioneering startup reshaping engineering with AI.
- Benefits: Full ownership of projects, high impact work, and collaboration with industry veterans.
- Other info: Dynamic team culture focused on integrity and impactful innovation.
- Why this job: Join us to redefine an industry and accelerate sustainable energy solutions.
- Qualifications: Master's in ML or related field, strong Python skills, and ML application experience.
The predicted salary is between 36000 - 60000 £ per year.
About BeyondMath
BeyondMath is a pioneering startup, backed by top-tier VCs, on a mission to reshape the frontiers of engineering through Foundational AI models for Physics. We are replacing traditional, slow and expensive simulation methods with AI that rivals accuracy at orders of magnitude higher speed. We are moving beyond the "generic AI" hype to solve the world’s hardest physical engineering challenges in automotive, aerospace, and energy.
The Role
As a Machine Learning Engineer, you’ll play a central role in advancing our Generative Physics simulation platform. You’ll work at the intersection of ML research and engineering—contributing to core model development, shaping model architecture, and delivering performant systems that integrate seamlessly into our real-world design optimization workflows. You'll work closely with our ML research team, software engineers, and industry partners to deploy robust, scalable models that deliver real-world impact.
Responsibilities
- Physics-Focused AI Model Development: Design and train deep learning models for physics simulation across aerodynamic and engineering domains.
- Scalability & Performance: Drive optimization efforts for model inference speed, accuracy, and robustness on large-scale industrial datasets.
- Geometry Representation: Research effective ways to represent geometric design variations for efficient use by machine learning models.
- Production Integration: Partner with engineering teams to deploy and monitor models in production-grade pipelines and tools.
- Architecture & Design: Contribute to design decisions around model and data architecture, tooling, and ML infrastructure.
Essential Requirements
- Industrial Experience: Strong track record applying ML to complex real-world problems (ideally including geometry or physical systems).
- Foundational Knowledge: Deep understanding of machine learning theory, including optimization, generalisation, and various model architectures.
- Programming: Strong python skills and experience with deep learning libraries (TensorFlow/PyTorch/JAX).
- Communication: Ability to clearly explain complex ML concepts and research findings to both technical and non-technical audiences.
- Education: Master's Degree (PhD preferred) in Machine Learning, Computer Science, or a related quantitative field.
Highly Desirable
- Aerodynamics/CFD Expertise: Familiarity with aerodynamic principles and computational fluid dynamics is a major plus.
- Design Optimization: Prior experience in optimization algorithms, particularly in the context of engineering design.
- Physics/Science ML: Experience integrating physical laws or constraints into machine learning models.
Why Join Us?
- Full Ownership: You will have a direct seat at the table in shaping the future of a company redefining an entire industry.
- High Impact: Your work will directly accelerate the transition to sustainable energy and more efficient transport.
- Elite Team: Work alongside veterans from world-leading AI labs and engineering firms in a culture of "impact with integrity."
Machine Learning Engineer in London employer: BeyondMath
BeyondMath is an exceptional employer for Machine Learning Engineers, offering a unique opportunity to work at the forefront of AI innovation in a collaborative and dynamic startup environment. With a strong focus on employee growth, you will have the chance to shape groundbreaking technology while being part of an elite team dedicated to making a real-world impact in sustainable energy and engineering. Our culture promotes full ownership and integrity, ensuring that your contributions are valued and recognised as we redefine the industry together.
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning Engineer in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with people on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly to hiring managers.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to machine learning and physics simulations. This will give potential employers a taste of what you can do and set you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Practice explaining complex ML concepts in simple terms, as you'll need to communicate effectively with both technical and non-technical teams.
✨Tip Number 4
Apply through our website! We love seeing passionate candidates who are eager to join our mission at BeyondMath. Make sure to tailor your application to highlight your relevant experience and how you can contribute to our innovative projects.
We think you need these skills to ace Machine Learning Engineer in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the Machine Learning Engineer role. Highlight your experience with ML models, especially in physics or engineering contexts. We want to see how your skills align with our mission at BeyondMath!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about AI in physics and how you can contribute to our team. Keep it engaging and relevant to the role – we love seeing your personality come through!
Showcase Relevant Projects:If you've worked on projects related to deep learning or physics simulations, make sure to showcase them. Include links to your GitHub or any publications. We’re keen to see your hands-on experience and how you tackle real-world problems!
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows you’re serious about joining our team at BeyondMath!
How to prepare for a job interview at BeyondMath
✨Know Your Physics
Brush up on your physics knowledge, especially in aerodynamics and computational fluid dynamics. Be ready to discuss how you can apply machine learning to solve complex physical problems, as this will show your understanding of the role's core focus.
✨Showcase Your ML Skills
Prepare to talk about your experience with deep learning libraries like TensorFlow or PyTorch. Have specific examples ready where you've optimised model performance or integrated physical laws into your models, as this will demonstrate your practical skills.
✨Communicate Clearly
Practice explaining complex ML concepts in simple terms. You might be asked to present your past projects to both technical and non-technical audiences, so being able to communicate effectively is key to making a strong impression.
✨Ask Insightful Questions
Prepare thoughtful questions about BeyondMath's approach to AI in engineering. This shows your genuine interest in the company and the role, and it gives you a chance to assess if their mission aligns with your career goals.
